基于云模型的用户双重聚类推荐算法

A user dual clustering recommendation algorithm based on cloud model

协同过滤是一种应用广泛的推荐算法,但存在着效率低和数据稀疏等问题。为解决这些问题,提出了一种改进的聚类推荐算法。该算法引用云模型,先从项目属性和用户属性两方面计算云模型期望、熵和超熵,并考虑到评分时间、评分高低和评分习惯等因素的影响,建立用户兴趣模型;接着,采用基于云模型的修正相似度量方法进行用户兴趣相似度比较,并使用K-means算法进行聚类;最后,利用参与预测人数的比例对公共项目进行推荐结果合并。在MovieLens上的实验结果表明,该算法不仅可以解决效率低和数据稀疏等问题,还提高了推荐的准确性。

Collaborative filtering is a widely used recommendation algorithm, but problems such as low efficiency and data sparseness still exist. In order to solve these problems, we present an improved clustering recommendation algorithm. The algorithm introduces a cloud model, in which the expecta- tion, entropy and hyper entropy are calculated according to the item attributes and user attributes di- mensions. To build up a user interest model, the influence of rating time, rating level and rating habits are also taken into account. Then the similarities of user interests are compared by the corrected similari- ty measurement based on cloud model, and the K-means algorithm is adopted to perform clustering. Fi- nally, the recommendation results of the public projects are merged by using the proportion of the par- ticipants who will make predictions. Experiment results on the MovieLens show that the algorithm can not only solve the problem of low efficiency and data sparseness but also improve the accuracy of the rec- ommendation results.